Results 121 to 130 of about 97,157 (255)

Designing Trustworthy AI Systems for PTSD Follow-Up

Technologies
Post-Traumatic Stress Disorder (PTSD) poses complex clinical challenges due to its emotional volatility, contextual sensitivity, and need for personalized care.
María Cazares, Jorge Miño-Ayala, Iván Ortiz, Roberto Andrade   +3 more
doaj   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Toward Trustworthy On-Device AI: A Quantization-Robust Parameterized Hybrid Neural Filtering Framework

Mathematics
Recent advances in deep learning have led to a proliferation of AI services for the general public. Consequently, constructing trustworthy AI systems that operate on personal devices has become a crucial challenge.
Sangwoo Hong, Seung-Wook Kim, Seunghyun Moon, Seowon Ji   +3 more
doaj   +1 more source

Building a trustworthy AI differential diagnosis application for Crohn's disease and intestinal tuberculosis. [PDF]

BMC Med Inform Decis Mak, 2023
Lu K, Tong Y, Yu S, Lin Y, Yang Y, Xu H, Li Y, Yu S.   +7 more
europepmc   +1 more source

Artificial Intelligence (AI) and Agribusiness: From Automation to Augmentation in a Global Context

Agribusiness, EarlyView.
Alexis H. Villacis
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

Advanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee, Hyeonbin Moon, Junhyeong Lee, Seunghwa Ryu   +3 more
wiley   +1 more source

Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects

IEEE Open Journal of the Communications Society
Federated Learning (FL) emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy.
Asadullah Tariq, Mohamed Adel Serhani, Farag M. Sallabi, Ezedin S. Barka, Tariq Qayyum, Heba M. Khater, Khaled A. Shuaib   +6 more
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Ethical funding for trustworthy AI: proposals to address the responsibilities of funders to ensure that projects adhere to trustworthy AI practice. [PDF]

AI Ethics, 2022
Gardner A, Smith AL, Steventon A, Coughlan E, Oldfield M.   +4 more
europepmc   +1 more source